System and method for complementing video compression using video diffusion

A computer-implemented method, the method comprising:

• receiving, at a computing device, values of a set of weights for a diffusion model, the weights having been previously generated by:
• reducing fidelity of training frames of training image data to create frames of reduced-fidelity training image data,
• encoding the frames of reduced-fidelity training image data to create frames of compressed reduced-fidelity training image data,
• training a first artificial neural network using the frames of compressed reduced-fidelity training image data where values of the weights are adjusted during the training;
• and
• configuring a second artificial neural network present on the computing using the values of the set of weights.

A method, comprising:

• receiving, at a computing device, values of a set of weights for a diffusion model, the weights having been previously generated by:
• reducing fidelity of training frames of training image data to create frames of reduced-fidelity training image data,
• encoding the frames of reduced-fidelity training image data to create frames of compressed reduced-fidelity training image data,
• deriving a first set of data from the frames of training image data,
• training a first artificial neural network using the frames of compressed reduced-fidelity training image data in combination with the first set of data where the values of the set of weights are adjusted during the training and wherein the first set of data includes less data than the frames of training image data;
• configuring a second artificial neural network present on the computing using the values of the set of weights;
• receiving, at the computing device, a second set of data derived from frames of image data containing at least some scene information present in the training frames of training image data wherein the second set of data includes less data than the frames of image data;
• generating, by the second artificial neural network, reconstructed frames of compressed image data;
• and
• decoding the reconstructed frames of compressed image data to produce reconstructions of the frames of image data.

A computer-implemented method, comprising:

• generating a set of weights for a diffusion model, the generating including:
• reducing fidelity of training frames of training image data to create frames of reduced-fidelity training image data,
• encoding the frames of reduced-fidelity training image data to create frames of compressed reduced-fidelity training image data,
• deriving a first set of data from the frames of training image data,
• training a first artificial neural network using the frames of compressed reduced-fidelity training image data in combination with the first set of data where the values of the set of weights are adjusted during the training and wherein the first set of data includes less data than the frames of training image data;
• sending the values of the weights to a computing device configured to insert the values of the weights into one or more layers of a second artificial neural network;
• deriving a second set of data from frames of image data containing at least some scene information present within the frames of training image data wherein the second set of data includes less data than the frames of image data;
• sending the second set of data to the computing device wherein the second artificial neural network is configured to use the second set of data to generate reconstructed frames of compressed image data useable by a decoder to produce reconstructions of the frames of image data.